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This paper introduces the Acupuncture Anesthesia Knowledge Database (AAKD), a publicly available database integrating clinical data, mechanism research data, omics datasets, and articles related to acupuncture anesthesia (AA) for enhanced recovery after surgery. The database includes a large collection of data points across various species, genes, surgeries, diseases/symptoms, and acupoints. AAKD aims to address the lack of standardized guidelines and unclear acupuncture mechanisms, potentially supporting evidence-based practice and mechanism research in AA.
The creation of the AAKD provides a comprehensive, standardized, and publicly accessible database that may facilitate research and clinical decision-making regarding the use of acupuncture anesthesia in surgery.
Acupuncture anesthesia (AA) is an important practical application of the enhanced recovery after surgery concept, and it has demonstrated considerable potential in perioperative management by reducing anesthetic dosage, enhancing organ protection, and lowering complications. However, the field still faces challenges such as a lack of high‐quality evidence‐based medical data, the absence of standardized guidelines, and unclear acupuncture mechanisms, which hinder further advancements in clinical efficacy prediction and personalized application. To systematically integrate heterogeneous multi‐source knowledge in AA, this study proposed the first publicly available Acupuncture Anesthesia Knowledge Database (AAKD). AAKD adheres to a comprehensive development process, including public data collection, extraction, curation and evaluation, standardization, a multi‐dimensional quality control system construction, data analysis, and integration. AAKD includes 17,921 clinical data, 29,462 mechanism research data, 143 omics datasets, and 2034 articles. It covers 12 species, 1068 genes, 111 surgeries, 227 diseases/symptoms, and 260 acupoints. Taking IL1B gene as an example, it demonstrates a potential way for AAKD in integrating multi‐level data, offering knowledge support for users exploring basic research or clinical decision‐making. It also reveals cross‐module association in facilitating the discovery of new scientific questions. AAKD is a visual, systematic, and standardized platform, which has the potential to support the evidence‐based practice and mechanism research in AA, and lay the foundation for future artificial intelligence–driven analgesic strategies. AAKD is available at http://www.biomedinfo.cn/AAKD/index_en.php or http://www.bmtongji.cn/AAKD/index_en.php .
